ajoysr/bangla-math-gemma

Bangla Math Solver - Fine-tuned Gemma-2B Model

Model Overview

This model is a fine-tuned version of unsloth/gemma-2-2b-it-bnb-4bit specifically designed for solving mathematical problems in Bengali language. The model demonstrates strong performance in understanding Bengali mathematical word problems and providing step-by-step solutions.

Model Details

• Model Name: Bangla Math Solver
• Base Model: unsloth/gemma-2-2b-it-bnb-4bit
• Language: Bengali (বাংলা)
• Domain: Mathematics Problem Solving
• License: Apache 2.0
• Developed by: ajoysr
• Training Framework: Unsloth + Hugging Face TRL
• Hardware: Google Colab T4 GPU (Free Tier)

Dataset Information

• Source Dataset: hamim-87/Ashrafur_bangla_math
• Training Samples: 5,000 samples (first 5K rows)
• Data Format: Problem-solution pairs in Bengali
• Problem Types: Mathematical word problems, arithmetic, algebra, combinatorics

Sample Data Structure

Problem: 5 জন ছাত্র 3টি খেলার প্রতিযোগিতায় অংশগ্রহণের জন্য সাইন আপ করছে...
Solution: এই সমস্যা সমাধান করার জন্য, আমরা গুণন নিয়ম ব্যবহার করে গণনা নীতি প্রয়োগ করি...

Training Details

Training Configuration

• Epochs: Optimized for convergence
• Learning Rate: Adaptive scheduling
• Batch Size: Optimized for T4 GPU memory
• Sequence Length: 2048 tokens
• Training Time: ~2x faster with Unsloth optimization
• Memory Optimization: 4-bit quantization (BNB)

Training Environment

• Platform: Google Colab (Free Tier)
• GPU: NVIDIA T4 (16GB VRAM)
• Memory Management: Gradient checkpointing enabled
• Mixed Precision: Automatic mixed precision (AMP)

Model Performance

The model excels at:

• Understanding Bengali mathematical terminology
• Breaking down complex word problems into steps
• Providing detailed mathematical explanations in Bengali
• Handling various mathematical domains (arithmetic, algebra, combinatorics)
• Maintaining mathematical accuracy while explaining in native language

Usage

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "ajoysr/bangla-math-solver"  # Replace with your actual model name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage
problem = "একটি ত্রিভুজের তিনটি বাহুর দৈর্ঘ্য 3, 4, এবং 5 একক। এর ক্ষেত্রফল কত?"

# Format input
input_text = f"Problem: {problem}\nSolution:"
inputs = tokenizer(input_text, return_tensors="pt")

# Generate solution
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=512,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

solution = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(f"Solution: {solution}")

Input Format

The model expects input in the following format:

Problem: [Bengali mathematical problem]
Solution:

Technical Implementation

Key Features

• Efficient Training: Leveraged Unsloth for 2x faster training
• Memory Optimization: 4-bit quantization for resource efficiency
• Bengali Language Support: Specialized tokenization for Bengali text
• Mathematical Reasoning: Step-by-step problem-solving approach

Optimization Techniques

• LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning
• Gradient checkpointing for memory efficiency
• Dynamic padding for optimal batch processing
• Learning rate scheduling for stable convergence

Methodology

Data Preprocessing

1. Data Loading: Utilized first 5K samples from the Ashrafur_bangla_math dataset
2. Text Formatting: Structured problem-solution pairs with clear delimiters
3. Tokenization: Applied Bengali-aware tokenization with appropriate padding
4. Quality Control: Implemented data validation to ensure problem-solution alignment

Fine-tuning Strategy

1. Base Model Selection: Chose Gemma-2B for optimal balance of performance and efficiency
2. Parameter-Efficient Training: Applied LoRA adapters to reduce trainable parameters
3. Hyperparameter Optimization: Tuned learning rate, batch size, and sequence length
4. Convergence Monitoring: Implemented loss tracking and validation metrics

Evaluation Approach

• Mathematical accuracy assessment
• Bengali language fluency evaluation
• Step-by-step reasoning quality analysis
• Computational efficiency measurement

Limitations

• Trained specifically on Bengali mathematical problems
• Performance may vary on mathematical domains not well-represented in training data
• Limited to text-based mathematical problems (no geometric diagrams)
• Optimized for Google Colab T4 environment

Future Improvements

• Expand training dataset to include more diverse mathematical problems
• Add support for geometric problems with diagram interpretation
• Implement multi-turn conversation capability for clarifying questions
• Optimize for deployment on edge devices

Citation

If you use this model in your research, please cite:

@misc{bangla-math-solver-2025,
  title={Bangla Math Solver: Fine-tuned Gemma-2B for Bengali Mathematical Problem Solving},
  author={ajoysr},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/ajoysr/bangla-math-solver}
}

Acknowledgments

• Unsloth Team: For providing efficient fine-tuning framework
• Hugging Face: For model hosting and TRL library
• Dataset Creator: hamim-87 for the Ashrafur_bangla_math dataset
• Google Colab: For providing free GPU access for research

Contact

For questions, suggestions, or collaborations, please reach out through the Hugging Face model page or create an issue in the associated repository.

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Note: This model is designed for educational and research purposes. Always verify mathematical solutions for critical applications.